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IIT Delhi deep-tech startup CYRAN AI Solutions has introduced a one of its kind do-it-yourself (DIY) artificial intelligence (AI) kit for school students in order to help them learn and build smart products using the technology. The product goes by the name BUDDHI Kit, which stands for Build Understand Design Deploy Human-Like Intelligence. The Kit will help students in experiencing artificial intelligence, learning AI basics and building AI-based projects in a fun way, says IIT Delhi. The product is the brainchild of Professor Manan Suri of IIT Delhi, who was recognized by the Massachusetts Institute of Technology (MIT), USA as one of the world's top 35 innovators under the age of 35. The artificial intelligence kit was launched earlier this week by Professor V Ramgopal Rao, Director, IIT Delhi in the presence of representatives from education, skills NGOs, industry, schools, teachers and students.

Those that implement AI and Machine Learning project learn quickly that machine learning projects are not application development projects. Much of the value of machine learning projects rest in the models, training data, and configuration information that guides how the model is applied to the specific machine learning problem. The application code is mostly a means to implement the machine learning algorithms and "operationalize" the machine learning model in a production environment. That's not to say that application code is not necessary -- after all, the computer needs some way to operationalize the machine learning model -- but focusing a machine learning project on the application code is missing the big picture. If you want to be AI-first for your project, you need to have a data-first perspective.

Learn to use functions and apply Codes. This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application . Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.

In this course you will learn all the machine learning models that has vast applications and mostly used. We will cover all the mathematics behind every Machine Learning Model so that you understand what actually happens behind the scene and how we actually train the machine to make future decision. We will then move on to implement all machine learning models in Python. After taking this course, you guys should be able to know not just the implementation part but also you will have a genuine understanding of every model behind the scenes.

In this course you will learn all the machine learning models that has vast applications and mostly used. We will cover all the mathematics behind every Machine Learning Model so that you understand what actually happens behind the scene and how we actually train the machine to make future decision. We will then move on to implement all machine learning models in Python. After taking this course, you guys should be able to know not just the implementation part but also you will have a genuine understanding of every model behind the scenes.

Natalie Lao was set on becoming an electrical engineer, like her parents, until she stumbled on course 6.S192 (Making Mobile Apps), taught by Professor Hal Abelson. Here was a blueprint for turning a smartphone into a tool for finding clean drinking water, or sorting pictures of faces, or doing just about anything. "I thought, I wish people knew building tech could be like this," she said on a recent afternoon, taking a break from writing her dissertation. After shifting her focus as an MIT undergraduate to computer science, Lao joined Abelson's lab, which was busy spreading its App Inventor platform and do-it-yourself philosophy to high school students around the world. App Inventor set Lao on her path to making it easy for anyone, from farmers to factory workers, to understand AI, and use it to improve their lives.

Over the past few years, news of the success of data-fed virtual teaching assistants and smart enrollment counselor chatbots has had the higher education world abuzz with the possibilities inherent in using artificial intelligence on campus. Colleges and universities hope AI will help them offload time-intensive administrative and academic tasks, make IT processes more efficient, boost enrollment in a climate of decline and deliver a better learning experience for students. On some campuses, these improvements are already taking place. While scaling up AI deployment at universities will take time due to the costs involved, some faculty members may also be resistant to AI on campus because they worry it will put them out of their jobs. The best way to convince potential stakeholders of the need for AI is to "opt for a problem-first approach," suggests the Education Advisory Board, an education enrollment services and research company.

Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well examined concept in academia. Historically, the focus has been on theoretical studies in the context of monopolistic firms and assumed product relationships, e.g., complementarity in usage. We develop a new machine-learning-driven methodology for designing bundles in a large-scale, cross-category retail setting. We leverage historical purchases and consideration sets created from clickstream data to generate dense continuous representations of products called embeddings. We then put minimal structure on these embeddings and develop heuristics for complementarity and substitutability among products. Subsequently, we use the heuristics to create multiple bundles for each product and test their performance using a field experiment with a large retailer. We combine the results from the experiment with product embeddings using a hierarchical model that maps bundle features to their purchase likelihood, as measured by the add-to-cart rate. We find that our embeddings-based heuristics are strong predictors of bundle success, robust across product categories, and generalize well to the retailer's entire assortment.

-- We introduce for the first time the utilization of Long short - term memory (LSTM) neural network architectures for the compensation of fiber nonlinearities in digital coherent systems. We conduct numerical simulations considering either C - band or O - band transmission systems for single channel and multi - channel 16 - QAM modulation format with polarization multiplexing . A detailed analysis regarding the effect of the number of hidden units and the length of the word of sym bols that trains the LSTM algorithm and corresponds to the considered channel memory is conducted in order to reveal the limits of LSTM based receiver with respect to performance and complexity. The numerical results show that LSTM Neural Networks can be v ery efficient as post processors of optical receivers which clas sify data that have undergone non - linear impairments in fiber and provide superior performance compared to digital back propagation, especially in the multi - channel transmission scenario. The complexity analysis shows that LSTM becomes more complex as the number of hidden units and the channel memory increase can be less complex than DBP in long distances ( 1000 km). There is a huge effort in fiber - optic communication industry to cope with the exponentially increasing capacity demands coming from next generation mobile networks and high bandwidth internet applica tions [1]. New trends such as internet of things especially in the context of tactile internet increase the requirements for real - time, high bandwidth, high availability connectivity in the access network domain, thus enhancing the capacity needs in metro and long - haul transmission networks. Optical fiber communication community predicted the imminent explosion of capacity needs ten years ago and started working intensively on techniques that can leverage fiber capabilities in this respect.